/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/distributed_runtime/collective_rma_distributed.h"

#include <memory>

#include "absl/status/status.h"
#include "tensorflow/core/common_runtime/base_collective_executor.h"
#include "tensorflow/core/common_runtime/copy_tensor.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/dma_helper.h"
#include "tensorflow/core/common_runtime/process_util.h"
#include "tensorflow/core/distributed_runtime/call_options.h"
#include "tensorflow/core/distributed_runtime/cancellable_call.h"
#include "tensorflow/core/distributed_runtime/request_id.h"
#include "tensorflow/core/distributed_runtime/worker_cache.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/platform/protobuf_internal.h"
#include "tensorflow/core/profiler/lib/scoped_memory_debug_annotation.h"
#include "tensorflow/core/protobuf/transport_options.pb.h"
#include "tensorflow/core/protobuf/worker.pb.h"

namespace tensorflow {

namespace {

class RecvBufCall : public CancellableCall {
 public:
  RecvBufCall(int64_t step_id, const string& peer_device,
              const string& peer_task, const string& key, Device* to_device,
              DeviceContext* to_device_ctx,
              const AllocatorAttributes& to_alloc_attr, Tensor* to_tensor,
              const DeviceLocality& client_locality,
              const DeviceAttributes& server_attributes,
              CancellationManager* cancel_mgr, WorkerCacheInterface* wc)
      : CancellableCall(cancel_mgr, peer_task, wc) {
    req_.set_step_id(step_id);
    req_.set_buf_rendezvous_key(key);
    *req_.mutable_client_locality() = client_locality;
    *req_.mutable_server_locality() = server_attributes.locality();
    req_.set_num_bytes(to_tensor->TotalBytes());
    req_.set_buf_ptr(reinterpret_cast<int64_t>(DMAHelper::base(to_tensor)));
    req_.set_src_device(peer_device);
    req_.set_src_incarnation(server_attributes.incarnation());
    req_.set_dst_device(to_device->name());
    req_.set_request_id(GetUniqueRequestId());
  }

  ~RecvBufCall() override {}

  void IssueCall(const StatusCallback& done) override {
    wi_->RecvBufAsync(&opts_, &req_, &resp_, done);
  }

  RecvBufRequest req_;
  RecvBufResponse resp_;
};

void PopulateTensorFromExtra(const RecvBufRespExtra& extra,
                             Tensor* cpu_tensor) {
  char* head = reinterpret_cast<char*>(DMAHelper::base(cpu_tensor));
  for (const auto& tensor_content_chunk : extra.tensor_content()) {
    memcpy(head, std::string(tensor_content_chunk).data(),
           tensor_content_chunk.size());
    head += tensor_content_chunk.size();
  }
}

Status PopulateTensorFromResponse(const RecvBufResponse& response,
                                  Tensor* cpu_tensor) {
  const bool has_transport_options = response.has_transport_options();

  // If there are no transport options, then the tensor has already been
  // copied into request.buf_ptr.
  if (!has_transport_options) return OkStatus();

  const int64_t total_bytes = cpu_tensor->TotalBytes();
  int64_t num_bytes = 0;
  RecvBufRespExtra extra;
  response.transport_options().UnpackTo(&extra);
  for (const auto& chunk : extra.tensor_content()) {
    num_bytes += chunk.size();
  }

  if (num_bytes != total_bytes) {
    return errors::Internal("Tensor Size Mismatch: RecvBufResponse returned ",
                            num_bytes,
                            " bytes, expected: ", cpu_tensor->TotalBytes());
  }
  PopulateTensorFromExtra(extra, cpu_tensor);
  return OkStatus();
}

}  // namespace

void CollectiveRemoteAccessDistributed::RecvFromPeer(
    const string& peer_device, const string& peer_task, bool peer_is_local,
    const string& key, Device* to_device, DeviceContext* to_device_ctx,
    const AllocatorAttributes& to_alloc_attr, Tensor* to_tensor,
    const DeviceLocality& client_locality, int dev_to_dev_stream_index,
    CancellationManager* cancellation_manager, const StatusCallback& done) {
  if (peer_is_local) {
    CollectiveRemoteAccessLocal::RecvFromPeer(
        peer_device, peer_task, peer_is_local, key, to_device, to_device_ctx,
        to_alloc_attr, to_tensor, client_locality, dev_to_dev_stream_index,
        cancellation_manager, done);
    return;
  }

  // State that needs to be threaded through a couple of async calls
  // in order to make this function completely non-blocking.
  struct State {
    DeviceAttributes server_attributes;
    std::unique_ptr<RecvBufCall> call;
    std::unique_ptr<Tensor> cpu_tensor;
  };
  State* state = new State;

  DeviceAttributes server_attributes;
  Status s = dev_resolver_->GetDeviceAttributes(peer_device,
                                                &state->server_attributes);
  if (!s.ok()) {
    delete state;
    done(s);
    return;
  }

  Tensor* dst_tensor = nullptr;
  Device* cpu_dev = nullptr;
  if (to_device->tensorflow_accelerator_device_info()) {
    // Move the bytes into a CPU tensor then use tensor-to-tensor copy.
    // Use GPU-registered memory for the CPU tensor so the transfer
    // goes faster.

    Status status = dev_mgr_->LookupDevice("CPU:0", &cpu_dev);
    if (!status.ok()) {
      delete state;
      done(s);
      return;
    }
    AllocatorAttributes cpu_attr;
    cpu_attr.set_gpu_compatible(true);
    profiler::ScopedMemoryDebugAnnotation op_annotation(
        "CollectiveRemoteAccessDistributed::RecvFromPeer"
        "::recv_buf_callback",
        step_id_, "dynamic", to_tensor->dtype(),
        [to_tensor]() { return to_tensor->shape().DebugString(); });

    state->cpu_tensor =
        std::make_unique<Tensor>(cpu_dev->GetAllocator(cpu_attr),
                                 to_tensor->dtype(), to_tensor->shape());
    dst_tensor = state->cpu_tensor.get();
  } else {
    dst_tensor = to_tensor;
  }

  // Logic to be executed on the RecvBufAsync callback.
  auto recv_buf_callback =
      [this, state, to_device, to_alloc_attr, to_device_ctx, to_tensor, cpu_dev,
       dev_to_dev_stream_index, dst_tensor, done](const Status& s) {
        if (s.ok()) {
          // In this generic implementation the bytes come back in one of 2
          // ways:
          // 1. In the response protobuf transport_options field (OR)
          // 2. It has already been copied over into RecvBufCall::req_.buf_ptr()
          // provided in request. buf_ptr is set to dst_tensor and points to
          // either the temporary cpu_tensor in case to_device is a GPU device
          // OR directly to to_tensor if to_device is not a GPU device.
          //
          // PopulateTensorFromResponse handles both cases.
          // (NOP in 2nd case) In case the final to_tensor is on GPU, buf_ptr
          // points to a tmp CPU buffer and needs to be copied over to
          // to_tensor.
          Status status =
              PopulateTensorFromResponse(state->call->resp_, dst_tensor);
          if (!status.ok()) {
            done(status);
            delete state;
            return;
          }

          if (to_device->tensorflow_accelerator_device_info()) {
            AllocatorAttributes cpu_attr;
            cpu_attr.set_gpu_compatible(true);
            CopyTensor::ViaDMA("",  // edge name (non-existent)
                               nullptr /*send_dev_ctx*/, to_device_ctx, cpu_dev,
                               to_device, cpu_attr, to_alloc_attr, dst_tensor,
                               to_tensor, dev_to_dev_stream_index,
                               [this, state, done](const Status& s) {
                                 delete state;
                                 // This callback must not block, so execute
                                 // done in another thread.
                                 work_queue_->Schedule([s, done] { done(s); });
                               });
            return;
          }
        }
        delete state;
        done(s);
      };

  state->call.reset(new RecvBufCall(
      step_id_, peer_device, peer_task, key, to_device, to_device_ctx,
      to_alloc_attr, dst_tensor, client_locality, state->server_attributes,
      cancellation_manager, worker_cache_));
  CancellationToken abortion_token =
      abortion_cancel_mgr_.get_cancellation_token();
  bool already_aborted = !abortion_cancel_mgr_.RegisterCallback(
      abortion_token, [state] { state->call->Cancel(); });
  if (already_aborted) {
    recv_buf_callback(errors::Cancelled("collective ops already aborted"));
  } else {
    state->call->Start([this, abortion_token,
                        done = std::move(recv_buf_callback)](const Status& s) {
      abortion_cancel_mgr_.DeregisterCallback(abortion_token);
      done(s);
    });
  }
}

void CollectiveRemoteAccessDistributed::CheckPeerHealth(
    const string& peer_task, int64_t timeout_in_ms,
    const StatusCallback& done) {
  if (peer_task == task_name_) {
    // Fast path if the peer is the worker itself.
    done(OkStatus());
    return;
  }
  // We send a GetStatus RPC to check the health of a peer task. If the RPC
  // succeeds, we verify if the peer_device incarnation matches the local record
  // if we have it. Note that DeviceResolverInterface always caches the device
  // attributes.
  WorkerInterface* wi = worker_cache_->GetOrCreateWorker(peer_task);
  if (wi == nullptr) {
    done(errors::InvalidArgument(peer_task,
                                 " not found. It's probably invalid. The "
                                 "valid form is /job:xxx/replica:0/task:N"));
    return;
  }
  auto opts = new CallOptions();
  opts->SetTimeout(timeout_in_ms);
  auto req = new GetStatusRequest();
  auto resp = new GetStatusResponse();
  // Note that fail_fast is not always respected, so we set a timeout as well.
  // We're not using CancellableCall since check health shouldn't need to be
  // cancelled.
  wi->GetStatusAsync(
      opts, req, resp, /*fail_fast*/ true,
      [this, opts, req, resp, wi, peer_task, done](Status s) {
        std::vector<DeviceAttributes> cached_attrs;
        if (s.ok()) {
          s = dev_resolver_->GetAllDeviceAttributes(peer_task, &cached_attrs);
        }
        if (s.ok()) {
          absl::flat_hash_set<uint64> remote_incarnations;
          for (const DeviceAttributes& da : resp->device_attributes()) {
            remote_incarnations.insert(da.incarnation());
          }
          for (const DeviceAttributes& attr : cached_attrs) {
            if (!remote_incarnations.contains(attr.incarnation())) {
              s = errors::FailedPrecondition(
                  attr.name(), " with incarnation ", attr.incarnation(),
                  " is not available. This usually means ", peer_task,
                  " has restarted");
              break;
            }
          }
        } else if (absl::IsNotFound(s)) {
          // Skip validating device incarnation if we don't know what the
          // incarnation should be. The device attribute is cached after the
          // first collective.
          s = OkStatus();
        }
        delete opts;
        delete req;
        delete resp;
        worker_cache_->ReleaseWorker(peer_task, wi);
        done(s);
      });
}

void CollectiveRemoteAccessDistributed::StartAbort(const Status& s) {
  CollectiveRemoteAccessLocal::StartAbort(s);
  abortion_cancel_mgr_.StartCancel();
}

}  // namespace tensorflow
